Systems and methods for estimating magnetic susceptibility through continuous motion in an mri scanner

ABSTRACT

Systems and methods for estimating magnetic susceptibility of a patient through continuous motion in an MRI scanner are provided herein. In one or more examples, during the collection of data, the patient can be instructed to move their head or other part of the body in a continuous manner and for a fixed duration of time. During the fixed duration of time, magnitude a data from the RF signal can be received by one or more RF coils can be collected. The received and undersampled magnitude data can be converted to phase data which can then be converted to magnetic susceptibility. Thus magnetic susceptibility can be determined while allowing for continuous motion during the MRI scan, which can be more comfortable and feasible for the patient in contrast to techniques that require the patient to hold their body at a particular orientation in the scanner for a fixed duration of time.

FIELD OF THE DISCLOSURE

The present disclosure relates to systems and methods for recording andestimating magnetic susceptibility of human tissue of a patient using amagnetic resonance imaging (MRI) scanner configured to allow forcontinuous movement of the patient during a data collection process.

BACKGROUND OF THE DISCLOSURE

Magnetic Resonance Imaging (MRI) has been employed in the medical fieldto provide clinicians within detailed pictures of a patient's anatomyand images of their physiological process. In order to generate an imageusing MRI, a patient is placed into a chamber (e.g., MRI scanner) thatgenerates a strong magnetic field around the patient. The magnetic fieldcan cause hydrogen atoms within the patient's body to emit a radiofrequency (RF) signal which can be detected by one or more RF receivingcoils that are part of the MRI scanner. The magnitude and phase of theRF signal can be used to create an image of the internal anatomy andphysiological processes occurring within the body.

Clinicians traditionally have been interested in using the magnitudeinformation provided by an MRI scan to build detailed images of apatient's anatomy for the purposes of assessing and diagnosing a myriadof conditions. The magnitude of a received RF signal can be used todetermine the precise location, position, and condition of organs andother structures within a patient's body. Thus, the magnitudeinformation provided by an MRI scan can be used to provide detailedinternal images of the body.

While magnitude information can provide useful information to aclinician, the phase information produced by an MRI scan also provideuseful information about the patient being scanned. For instance, thephase information generated from an MRI scan can be used to measure themagnetic susceptibility of the tissue in the human body. The magneticsusceptibility of various tissues in the human body can be used todetermine the amount and concentration of various molecules in the humanbody such as iron, heme, copper, and oxygen and can also provideinformation regarding the temperature, flow, tissue elasticity, andmolecular content of the tissue being analyzed. Accurately recording theamount of concentration of various molecules in the human body canprovide clinicians with a powerful tool to diagnose and treat patientswith various pathologies.

However, deriving molecular composition from an MRI phase signal can bea difficult process. Generally, the first step involves calculatingmagnetic susceptibility from the MRI data. This is an ill-posed problemwhich requires injecting prior information in order to generateartifact-free images of tissue magnetic susceptibility. Variousapproaches to ‘regularize’ this process are known to yield vastlydifferent solutions. The gold standard in the in vivo mapping ofmagnetic susceptibility of tissue is a long and labor-intensive MRI scanwhich requires acquiring the same scan at different fixed headpositions. The subject is expected to remain still, while holding thispre-determined head position, for the duration of the MRI scan. Patientswith various disorders (particularly those with neurological disorders)generally cannot be expected to move their body into fixed positions andhold still for long periods of time. Generally, movement during any MRIscan leads to measurement errors. Together with the long acquisitiontime needed to obtain reliable magnetic susceptibility information,requiring patients to be motionless during an MRI scan further erodesthe current ability to use magnetic susceptibility information as adiagnostic tool.

What is needed is an MRI scanner and method for use of the scanner thatcan collect sufficient and reliable magnetic susceptibility informationfrom a patient in a manner that doesn't require the patient to engage ininviable and burdensome collection procedures.

SUMMARY OF THE DISCLOSURE

Accordingly, systems and methods for estimating magnetic susceptibilityof a patient through continuous motion in an MRI scanner is provided. Inone or more examples, the MRI scanner can be configured with a pulsesequence protocol that can allow for measurements of off-resonanceeffects as well as motion tracking hardware that can be used to trackthe users motion in the scanner as a function of time. In one or moreexamples, during the collection of data, the patient can be instructedto move their head or other part of the body in a continuous manner andfor a fixed duration of time. During the fixed duration of time, bothmagnitude and phase data from the RF signal received by one or more RFcoils can be collected.

In one or more examples, the data collected during the MRI scanningprocedure can be organized into one or more “bins” that are createdbased on the motion registration of the patient. (i.e., the range ofmotion that the user moved during the scanning procedure). Each bin canrepresent a narrow range of positions in two or more dimensions.

In one or more examples, the phase information for each bin can becalculated by using not only the data points contained within aparticular bin, but also data points that are found in adjacent bins,thereby providing sufficient data to overcome the loss of the collectedphase data. Once the phase information for each bin has been calculated,the information can be used to calculate the magnetic susceptibility oftissue within the patient thereby providing the clinician with usefulinformation that might have not been available with magnitude based MRIimaging.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary MRI system according to examples of thedisclosure.

FIG. 2 illustrates an exemplary process for obtaining a phase image froman MRI scanner according to examples of the disclosure.

FIG. 3 illustrates another exemplary process for obtaining a phase imagefrom an MRI scanner according to examples of the disclosure.

FIG. 4 illustrates MRI system configured to acquire magneticsusceptibility of tissue within a patient according to examples of thedisclosure.

FIG. 5 illustrates an exemplary method of collecting data from an MRIsystem configured to acquire magnetic susceptibility of tissue within apatient according to examples of the disclosure.

FIG. 6 illustrates an exemplary method of processing the data collectedfrom an MRI system configured to acquire magnetic susceptibility oftissue within a patient according to examples of the disclosure.

FIG. 7 illustrates an exemplary graphical representation of a binneddata set according to examples of the disclosure.

FIG. 8 illustrates an example of a computing device according toexamples of the disclosure.

DETAILED DESCRIPTION

In the following description of the disclosure and embodiments,reference is made to the accompanying drawings in which are shown, byway of illustration, specific embodiments that can be practiced. It isto be understood that other embodiments and examples can be practicedand changes can be made without departing from the scope of thedisclosure.

In addition, it is also to be understood that the singular forms “a,”“an,” and “the” used in the following description are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It is also to be understood that the term “and/or” as usedherein refers to and encompasses any and all possible combinations ofone or more of the associated listed items. It is further to beunderstood that the terms “includes,” “including,” “comprises,” and/or“comprising,” when used herein, specify the presence of stated features,integers, steps, operations, elements, components, and/or units but donot preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, units, and/or groupsthereof.

Some portions of the detailed description that follow are presented interms of algorithms and symbolic representations of operations on databits within a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps (instructions)leading to a desired result. The steps are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical, magnetic, or opticalsignals capable of being stored, transferred, combined, compared, andotherwise manipulated. It is convenient at times, principally forreasons of common usage, to refer to these signals as bits, values,elements, symbols, characters, terms, numbers, or the like. Furthermore,it is also convenient at times to refer to certain arrangements of stepsrequiring physical manipulations of physical quantities as modules orcode devices without loss of generality.

However, all of these and similar terms are to be associated with theappropriate physical quantities and are merely convenient labels appliedto these quantities. Unless specifically stated otherwise as apparentfrom the following discussion, it is appreciated that, throughout thedescription, discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining,” “displaying,” or the likerefer to the action and processes of a computer system, or similarelectronic computing device, that manipulates and transforms datarepresented as physical (electronic) quantities within the computersystem memories or registers or other such information storage,transmission, or display devices.

Certain aspects of the present invention include process steps andinstructions described herein in the form of an algorithm. It should benoted that the process steps and instructions of the present inventioncould be embodied in software, firmware, or hardware, and, when embodiedin software, could be downloaded to reside on and be operated fromdifferent platforms used by a variety of operating systems.

The present invention also relates to a device for performing theoperations herein. This device may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a non-transitory,computer-readable storage medium, such as, but not limited to, any typeof disk, including floppy disks, optical disks, CD-ROMs,magnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards,application-specific integrated circuits (ASICs), or any type of mediasuitable for storing electronic instructions and each coupled to acomputer system bus. Furthermore, the computers referred to in thespecification may include a single processor or may be architecturesemploying multiple processor designs for increased computing capability.

The methods, devices, and systems described herein are not inherentlyrelated to any particular computer or other apparatus. Variousgeneral-purpose systems may also be used with programs in accordancewith the teachings herein, or it may prove convenient to construct amore specialized apparatus to perform the required method steps. Therequired structure for a variety of these systems will appear from thedescription below. In addition, the present invention is not describedwith reference to any particular programming language. It will beappreciated that a variety of programming languages may be used toimplement the teachings of the present invention as described herein.

MRI technology has been used extensively to provide clinicians withdetailed images of the internal structures of the human body as well asphysiological processes occurring within the body. Images generated byMRI technology can be more detailed and precise than images generated byother technologies such as X-ray, thus allowing clinicians to make moreprecise and accurate diagnoses of various diseases and conditions.

The use of MRI technology to image the human body can include generatinga magnetic field that causes the hydrogen atoms within the tissue beingimaged to orient themselves with the magnetic field. Specifically, anoscillating magnetic field at a specific frequency (i.e., resonancefrequency) is applied to an area to be imaged. When applied at theresonance frequency the magnetic field causes some of the hydrogen atomsto align with the magnetic field, and causes some of the hydrogen atomsto align in opposition to the magnetic field. Once the hydrogen atoms inthe tissue being imaged have been aligned by the magnetic field, the MRIscanner will transmit one or more RF signals to the tissue being imaged.The RF signals can be absorbed by the hydrogen atoms that are aligned inopposition to the magnetic field, causing the hydrogen atoms to thenbecome aligned with the magnetic field. Once the RF signal is removed,the hydrogen atoms whose alignment was flipped by the RF signal, willreturn to their original orientation (i.e., in opposition to themagnetic field) and in turn will give off RF energy that can be detectedby one or more RF receiving coils in the MRI scanner. The magnitude ofthe RF signals detected by the RF coils can be proportional to theamount of tissue in a given area, and the MRI scanner can use thisinformation to create detailed images of the area being scanned.

FIG. 1 illustrates an exemplary MRI system according to examples of thedisclosure. The system 100 of FIG. 1 illustrates an exemplary systemconfigured to generate MRI images by a clinician. In the example of FIG.1, a subject 116 (the patient to be imaged) can be placed within achamber that includes one or more magnets 108 and one or more RF coils110. The one or more magnets 108 can be communicatively coupled to apulse sequencer 106. In one or more examples, the pulse sequencer 106can be configured to control the one or more magnets 108 so as togenerate a magnetic field in a particular area of the subject 116 thatis desired to be imaged. The pulse sequencer 106 can be configured togenerate a magnetic field at the desired frequency so as to cause thehydrogen atoms in the tissue being examined to orient themselves withrespect to the magnetic field.

In one or more examples, the one or more RF coils 110 can be alsocommunicatively coupled to pulse sequencer 106. The one or more RF coils110 can be configured to both transmit and receive RF pulses. In one ormore examples, the pulse sequencer 106 can be configured to cause theone or more RF coils 110 to transmit RF pulses directed at the tissuebeing imaged so as to cause the one or more hydrogen atoms that arealigned in opposition to the magnetic field to absorb the energyprovided by the RF pulses so as to cause their orientation to flip so asto become aligned with the magnetic field.

In one or more examples, the one or more RF coils 110 can be alsocommunicatively coupled to an image reconstruction unit 112 that can beconfigured to receive the signals received by the one or more RF coils110. As discussed above, when the hydrogen atoms whose orientation hasbeen flipped due to the RF pulses due to energy absorption return totheir original orientation thereby giving off RF energy, the RF coilscan receive the energy being expelled from the hydrogen atoms. In one ormore examples, the image reconstruction unit 112 can be configured tocollect the RF signals provided by the one or more RF coils 110 and usethe received signal to construct an image of the tissue being analyzed.

In one or more examples of the disclosure, both the pulse sequencer 106and the image reconstruction unit 112 can be communicatively coupled toan interface 104. The interface 104 can facilitate the exchange ofinformation between the components of the MRI system and an operator ofthe MRI system. In one or more examples, the interface 104 can receiveinputs 102 from a human operator of the MRI system. Inputs can includesetting various operating parameters such as specific pulse sequencesthat the MRI system will generate during its operation. The interface104 can also provide output data to the operator of the MRI system. Inone or more examples, the interface 104 can generate a visualization ofthe MRI image produced by the image reconstruction unit 112 which canthen be used by a clinician to diagnose and treat a patient.

The signal received by each RF coil ‘c’ 110 can be represented using thefollowing equation:

I _(c)(r)=T _(c)(r)m(r)e ^(iφ(r))   (equation 1)

In equation 1 above, r represents the spatial coordinates, m(r) canrepresent the magnitude of the image, and φ(r) can represent the phaseof the image and T_(c)(r) is the complex sensitivity of the RF coil ‘c’.Conventionally, MRI images are primarily concerned about the magnitudeof the signal rather than the phase of the signal. The majority ofimages analyzed currently by MRI users (e.g., clinicians, radiologists)are magnitude-domain images. Because the magnitude of the signal can beproportional to the density of the tissue at a particular location,magnitude-domain images can be very useful to help visualize both theanatomy and physiological processes that exist in the imaged area.

Phase information can also be useful to a clinician. In one or moreexamples, the received RF signals can arrive when they are expected to,while other RF signals can arrive with a delay which is captured in thephase information. The delay in receiving a signal (i.e., phasedistortion) can indicate to a clinician that the tissue from which theRF signal was received was resonating slightly out of frequency fromother tissue surrounding it. The delay can be caused by variousphenomenon occurring within the tissue being examined. For example, ifthere is a micro-bleed occurring in the tissue being examined then thetissue may contain excess blood. Blood contains iron, hemoglobin, ormore oxygen that can disturb the magnetic field locally in the areabeing examined. In one or more examples, the off-resonance effect causedby phenomenon such as a micro-bleed may not be captured clearly byexamining the changes in magnitude of a signal, but instead may becaptured more accurately in the phase information of a given signal.

Phase information can be used to determine the magnetic susceptibilityof tissue, which can be a useful metric that gives cliniciansinformation that they can use to treat and diagnose patients. Magneticsusceptibility analysis can be used to determine iron content in blood,calcification of tissue, oxygenation of tissue, and other phenomenon. InMRI images that are based on the magnitude of the signal, the phaseinformation is often discarded thus losing valuable information relatingto the magnetic susceptibility of tissue.

The transformation from susceptibility of tissue to phase data asobtained by an MRI scanner is a lossy process. In one or more examples,the phase information acquired during the MRI scan is first convertedinto a measurement of the local magnetic field. Specifically, therelationship between the local field and the phase information receivedby an MRI scanner can be characterized by equation 2 provided below.

$\begin{matrix}{{\delta{B(r)}} = \frac{\phi(r)}{2\pi\gamma TE}} & \left( {{equation}\mspace{20mu} 2} \right)\end{matrix}$

In equation 2 above, ≢B(r) can represent the change in the magneticfield flux density, ϕ(r) can represent the phase information received byan MRI scanner, TE can represent the echo time which the measurement ismade, and γ is the gyromagnetic ratio.

The total magnetic field flux can have two different contributions. Wewrite these contributions in equation 3 below.

δB(r)=δB _(X)(r)+δB ₀(r)   (equation 3)

The first contribution to δB(r) on the right side of equation 3,δB_(X)(r), is the field caused by magnetic susceptibility variations(such as the spatial variations in tissue magnetic susceptibility overthe brain), and δB₀(r) is an unknown spatially varying global frequencycomponent, which is due to factors such as coil offset, magnetic fieldimperfections, etc.

The magnetic field offset δB₀(r) contains no information about thedesired tissue magnetic susceptibility. The effects of δB₀(r) can thusbe removed through one of two different approaches. The first is apre-processing step, normally referred to as Background Field Removal.The objective of Background Field Removal is essentially to extractδB_(X)(r) from total magnetic field ≢7B(r) and thus feed δB_(X)(r) tothe following magnetic susceptibility inversion step. The secondapproach to removing the effects of δB₀(r) is through a single stepprocess, inherent to magnetic susceptibility reconstruction, as proposedbelow.

The relationship between changes in the local field and magneticsusceptibility can be characterized by equation 4 below:

HX=ΔB_(X)   (equation 4)

In equation 4 above, H_(θ) is the known dipole kernel associated withpatient's head position θ, and ΔB=F(δB_(X)), where F is the FourierTransform operator. In one or more examples, the dipole kernel may notbe an invertible process in that it destroys information (zeros in thefrequency domain) about the magnetic susceptibility that can bedifficult to recover without making prior assumptions about the objectbeing scanned. This phenomenon can be made more difficult due to noise.Thus, solving for magnetic susceptibility can be referred to as an “illconditioned problem.”

The goal is to calculate the magnetic susceptibility of the tissue Xfrom ΔB_(X). This task can be represented using equations 5 & 6 below,where H⁻¹ is the inverse of the dipole operator and F⁻¹ is the inverseFourier Transform operator:

X=H ⁻¹(ΔB _(X))   (equation 5)

X=F ⁻¹(X)   (equation 6)

Various solutions to the ill-conditioned inverse problem in equation 5have been proposed. They range from single-step approaches to multi-stepiterative solutions that heavily rely on injecting prior-knowledge aboutthe object, regularization, and other assumptions. The resultingsolutions are often biased, inconsistent and suffer from artifacts.

Alternatively, another approach to solve for the magnetic susceptibilityof the tissue X is to acquire MRI data at “n” different angles of thehead. This requires the patient to hold their head still for n differentacquisitions, which require n times longer than 1 acquisition. The mainmotivation behind this approach is the collection of the n differentdipole kernels constitute an invertible and well-conditioned operationthat could be more easily inverted in post-processing. Namely, theintersection of the null-space between the n different kernels is chosento be as small as possible. This operation can be described by Equation7.

$\begin{matrix}{{\begin{bmatrix}H_{1} \\\vdots \\H_{n}\end{bmatrix}X} = \begin{bmatrix}{\Delta B_{\chi_{1}}} \\\vdots \\{\Delta B_{\chi_{n}}}\end{bmatrix}} & \left( {{equation}\mspace{20mu} 7} \right)\end{matrix}$

Where H_(i) is the dipole kernel associated with each of the positionsof the patient's head and ΔB_(X) _(i) is the resulting field measured atthat position.

A solution to equation 7 exists as long as a sufficient number and rangeof angles are acquired. One such MRI procedure exists and is known asCalculation of Susceptibility through Multiple Orientation Sampling(COSMOS). The COSMOS procedure can generate mathematically sufficientdata by oversampling the magnetic field (and thereby the phase) frommultiple orientations. In other words, the COSMOS procedure can generatemathematically sufficient data to determine magnetic susceptibility of atissue by taking multiple snapshots of the magnetic field at multipleorientations relative to the tissue being analyzed.

FIG. 2 illustrates an exemplary process for collecting phase data froman MRI scanner according to examples of the disclosure. In the exampleof FIG. 2 process 200 can represent a procedure that an MRI scanneroperator uses to extract sufficient phase information for a tissuesample, so as to accurately determine its magnetic susceptibility. Inone or more examples, the process 200 can begin at step 202 wherein anoperator of the MRI scanner can configure a pulse sequencer (describedabove with respect to FIG. 1) to generate a pulse sequence thatfacilitates the optimal collection of phase data. In one or moreexamples, a pulse sequence protocol used to generate magnitude-domainimages may be different than a pulse sequence protocol meant to generatemagnetic susceptibility images or measurements. Thus, in order toconfigure the device to generate magnetic susceptibility measurement, inone or more examples, the operator, prior to beginning data acquisition,may need to adjust the pulse sequence protocol administered by the pulsesequencer so as to optimize it for phase collection. Additionally, inone or more examples, a pulse sequence protocol can be configured notonly to optimize it for phase collection but to also configure it tooptimize it to collect data from a particular portion of the patient'sbody such as the brain or the heart. In other words the pulse sequenceprotocol used to collect phase images of the brain may be different thanthe pulse sequence protocol used to collect phase images of the heart.Specifically, cardiac pulse sequences have to be motion gated or motioncorrected, and often times triggered by a physiological monitoring toolsuch as a cardiac pulse, or electrocardiogram.

Once the pulse sequence protocol has been configured so as to collectphase data at step 202, the process can move to step 204 wherein asubject (i.e., a patient) is placed into a scanner. In one or moreexamples, at step 204, the patient can be placed within the scanner at aparticular orientation depending on the desired portion of the body tobe scanned. For instance, if the area of interest is the brain, then atstep 204 when the patient is placed in the scanner, they can be placedin the scanner so that their head is positioned at a particularorientation. For instance, in one or more examples, the head can betilted to one side (i.e., tilted towards one shoulder the other, ortilted up or down) in a particular orientation. Once the subject hasbeen placed at the desired orientation at step 204, the process 200 canmove to step 206 wherein an MRI measurement is taken at the fixedorientation. Taking an MRI measurement can include generating theappropriate magnetic field and RF signals and receiving the RF signalsbeing released by the hydrogen atoms in the tissue being studied. In oneor more examples, the pulse sequencer configured at step 202 can be usedto generate and receive the appropriate signals by coordinating theactions of each of the components in the MRI scanner.

In one or more examples, and as described above, in order to generateenough measurements so as to solve the inverse problem, a singleorientation measurement may not be adequate to ensure that the MRI datais mathematically sufficient to reconstruct an accurate estimate of themagnetic susceptibility. Thus, in one or more examples, in order togenerate such sufficient data, measurements may have to be acquired atdifferent orientations.

In addition to collecting enough orientations, enough measurements mustbe collected per orientation in order to generate such mathematicallysufficient set. Traditional MRI protocols must collect all k-space datawithin a specific region in the Fourier domain in order to generate animage (magnitude or phase) at a particular resolution. Thus, in one ormore examples, once a measurement at the fixed orientation is acquiredat step 206, the process 200 can move to step 208, wherein adetermination is made as to whether the number of measurements taken atthe fixed orientation of step 206 is equal to or greater than apre-determined threshold. If it is determined that the number ofmeasurements is not equal to or greater than the pre-determinedthreshold, then the process can revert back to step 206 to take anadditional measurement at the same orientation. If however the number ofmeasurements at the fixed orientation is equal to or greater than thepre-determined threshold, then the process can move to step 210described in detail below. In one or more examples, the predeterminedthreshold of step 208 can be selected so as to ensure that the number ofmeasurements are adequate to provide mathematically sufficient data soas to solve the inverse problem and thus solve for the magneticsusceptibility of the tissue being imaged by the MRI scanner.

After ensuring that an adequate number of measurements has been taken ata particular orientation at step 208, the process 200 can move to step210 wherein a determination can be made as to whether data has beencollected at sufficient orientations so as to provide an adequate set ofdata to solve for magnetic susceptibility. Specifically, at step 210 adetermination can be made as to whether data has been collected from apredetermined number of orientations (i.e., a threshold). If it isdetermined at step 210 that measurement data has been taken from anadequate number of orientations, then the process can move to step 214wherein it is terminated. Otherwise, if it is determined that there arenot an adequate number of orientations so as to determine the magneticsusceptibility of the tissue being analyzed, then the process can revertto step 212 wherein the patient is instructed to change theirorientation. In the examples of a brain scan, the patient can be told toorient their head in a different orientation by changing the way theyare tilting their head. In one or more examples, once the patient haschanged their orientation at step 212, the process 200 can move back tostep 206 wherein a measurement is taken at the new orientation.

As illustrated by the process 200 of FIG. 2, the process of obtainingsufficient MRI data to calculate magnetic susceptibility can require thepatients to not only position their body in a plurality of fixedpositions, but can also require that the patient hold their body in aparticular orientation for a fixed duration of time so that enoughmeasurement data can be collected. These requirements can mean that thepatient has to hold their body in an uncomfortable position for longperiods of times, and may even have to orient their body in a variety ofpositions so as to acquire the required data. Thus, the processdescribed above with respect to FIG. 2 may not be clinically viable asit places the patient under too much duress and discomfort during theMRI scan. In one or more examples, the number of fixed orientations canbe 12 or more and the time required at each orientation to acquireenough data can take five to ten minutes, thus requiring a procedurethat can take up to an hour and require the patient to maintain a fixedposition for long periods of time.

Because the process described above with respect to FIG. 2 can require agreat deal of time and uncomfortable positioning, many methods fordetermining the magnetic susceptibility of tissue can approximate themagnetic susceptibility of tissue using only a single fixed position butmaking assumptions about the original object. However, the assumptionscan lead to large measurement errors or artifacts in the generated imagethat may be unacceptable.

In light of the limitations described above with respect to FIG. 2, whatis needed is a process that is both comfortable to the patient while atthe same time generating accurate magnetic susceptibility estimates. Asdescribed in further detail below, generating adequate data to determinethe magnetic susceptibility of tissue without requiring the patient toengage in a long and uncomfortable data acquisition process can requirean adjustment to the architecture of the MRI scanner, a change in themethod to collecting data from a patient, as well as an adjustment tothe method in which the data acquired by the scanning process is used tosolve for magnetic susceptibility.

FIG. 3 illustrates another exemplary process for obtaining a phase imagefrom an MRI scanner according to examples of the disclosure. FIG. 3illustrates a process 300 by which an MRI scanner can be used togenerate data while allowing for the patient to engage in continuousmotion during the collection process, and use the generated data todetermine the magnetic susceptibility of the tissue being scanned.

In one or more examples of the disclosure, the process 300 can begin atstep 302 wherein the patient is instructed to continuously move one ormore parts of their body (such as their head) while the MRI scannerobtains measurements from the patient while they are continuouslymoving. As will be described below with respect to FIG. 4, the MRIdevice can be configured to include one or more components that trackthe movements of the user's body such that the exact position of theuser's body can be known and associated with the one or moremeasurements taken with the scanner.

In one or more examples of the disclosure, after acquiring MRI data froma patient continuously moving during the scanning process at step 302,the process can move to step 304 wherein the acquired data can be“registered.” In one or more examples, “registering” can refer to theprocess of associating each MRI data point with a position of the user'sbody, and then placing the MRI data point (with the known position) intoone or more bins for use in reconstructing a phase image. By registeringeach data point (i.e., associating it with a measured orientation of theuser's body) the obtained data can be associated with motion taken fromthe patient's frame of reference rather than the scanner's frame ofreference (described in further detail below).

Once each data point has been registered at step 304, the process 300can move to step 306 wherein the registered data can be used to recoverthe quantitative magnetic susceptibility of the tissue being sampled. Inone or more examples, step 306 can include two additional steps 308 and310. In order to recover the magnetic susceptibility at step 306, aphase image of the tissue being scanned can be reconstructed using theregistered motion-tracked data acquired at step 304 (described infurther detail below). As described in further detail below, in order tocreate the phase image reconstruction, the process 300 at step 308 canuse the registered data that is placed into one or more bins based onthe position of the user's body to reconstruct a phase image of thetissue being scanned. A particular method of recovering magneticsusceptibility is provided below, however the disclosure should not beseen as limited to the example, and other methods of recovering magneticsusceptibility from MRI data acquired during a continuous scan may beapplicable. For example, in one or more examples, recovering thequantitative susceptibility can include a single step method that canjointly combine under-sampled phase and magnitude data over angles/timeinto one magnetic susceptibility volume. In one or more examples of thedisclosure, recovering the quantitative susceptibility can include usinga machine learning approach that can cake in MRI data and generate amagnetic susceptibility volume. In one or more examples of thedisclosure, recovering the quantitative susceptibility can include usingphase image across all collected angles and turning the phase imagesinto a collection of phase images across a subset of angles (syntheticangles not necessarily encountered during collection) and thenreconstructing the resulting images into one magnetic susceptibilityvolume.

In one or more examples (and described in detail below), once a phaseimage has been reconstructed at step 308, the reconstructed phase imagecan be used to recover the magnetic susceptibility of the tissue, asdescribed below in more detail. Step 310 should be understood as anexemplary application of using the generated phase image acquired atstep 308 and should not be seen as limiting. In one or more examples,the phase image generated at step 308 using data that is acquired bycontinuous motion of the patient can be used for other purposes withoutdeparting from the scope of the disclosure. In the example of FIG. 3,after recovering the magnetic susceptibility of the tissue at step 310,the process can move to step 312 wherein the process 300 is terminated.

As discussed above with respect to FIG. 2, in order to generatemathematically sufficient data to provide accurate magneticsusceptibility measurements, data may need to be acquired at multipleorientations. However, in order to alleviate the issues with requiringdata to be acquired at fixed positions, it can be more viable to havethe patient move their body in a continuous manner thereby reducing thestrain on the patient. However, instructing a patient to move their bodyin a continuous manner can present challenges for generating a phaseimage. In the example of FIG. 2, the patient can be instructed to movetheir body into a very specific position thereby allowing the MRIoperator to know the exact position of the body at any given time withina reasonable degree of certainty. However, if the patient is asked tomove their body continuously, the exact position of the body and anygiven moment in time may not be accurately known. Thus, in one or moreexamples, the MRI scanner architecture described above with respect toFIG. 1 can be modified so as to provide the device with accurateinformation as to the orientation of the body at any given moment oftime during the data acquisition process.

FIG. 4 illustrates a MRI system configured to acquire magneticsusceptibility of tissue within a patient according to examples of thedisclosure. In the example of FIG. 4, MRI system 400 can includesubstantially the same components as the MRI system 100 of FIG. 1. Inone or more examples, components 402, 404, 406, 408, 410, 412, and 414can operate in substantially the same manner as their counterparts 102,104, 106, 108, 110, 112, and 114 respectively, and thus the descriptionabove with respect to the components of FIG. 1 can be referenced for adescription of their operation. In one or more examples, the pulsesequencer 406 can be configured with a pulse sequence protocol that isconfigured to obtain accurate phase data from the MRI scanner.Furthermore in one or more examples, and as described in further detailbelow, image reconstruction unit 412 can be configured to calculatemagnetic susceptibility from data generated through continuous movementof the patient so as to produce accurate magnetic susceptibilitymeasurements.

In one or more examples, MRI system 400 can additionally include amotion tracker 418 which can track the motion of a patient 416 in thescanner. Specifically, motion tracker 418 can be configured to track theorientation of a patient's body or portion of the body (such as thehead) and any given moment in time. Thus, in one or more examples,motion tracker 418 can generate data that indicates what the orientationof the body was as a function of time. As will be discussed in furtherdetail below, the data generated by the motion tracker 418 can be usedto process the MRI data so as to generate accurate magneticsusceptibility readings. In one or more examples, the motion tracker caninclude a camera that can be configured to track the body part ofinterest (such as the head), and determine the position of the head atany given moment in time. In one or more examples, the motion trackercan be configured to read one or more magnetically sensitive barcodesthat are applied to the patient that can show up in the image taken fromthe camera and be used to determine the position of the body part at anygiven moment in time. Generally, the motion tracker 418 can beconfigured to give the MRI scanner the position of the body part at anygiven moment in time within a reasonable degree of accuracy.

The system architecture provided by FIG. 4 can allow for a simpler andpatient friendly data collection process. By modifying the MRI scannerwith respect to its pulse generation sequence and the addition of motiontracking, the process of collecting data can be simplified so that itdoes not require the patient to orient their body in awkward oruncomfortable positions for long periods of time. Specifically, themodified MRI scanner can allow for the patient to continuously movetheir head during the data acquisition phase of the procedure which canbe more comfortable for the patient, take less time than previous phasecollection process, and produce magnetic susceptibility images that canme more accurate than conventional MRI phase scans.

FIG. 5 illustrates an exemplary method of collecting data from an MRIsystem configured to acquire magnetic susceptibility of tissue within apatient according to examples of the disclosure. In the example of FIG.5, the process 500 described below can be utilized in an MRI system thathas been configured to generate magnetic susceptibility data from apatient as described above with respect to FIG. 4. The process 500 ofFIG. 5 can begin at step 502 wherein the pulse sequencer unit of the MRIdevice is programmed by an operator with a pulse sequence that isconfigured to collect MRI phase data. In one or more examples, the pulsesequence generated by the pulse sequence generator can be robust tomotion and enable a retrospective binning of the data (as will befurther described below.) Furthermore in one or more examples, thespecific pulse sequencer can be dependent on the body part being imaged.Thus, while one pulse sequence can be used to generate phase images ofthe brain, a different pulse sequence may need to be configured to takephase images of the heart.

Once the pulse sequencer has been configured at step 502, the process500 can move to step 504 wherein the patient is placed into the MRIscanner. In one or more examples, the precise position of the patientwhen they enter the scanner may not be important as the patient willultimately continuously move during the scanning procedure and thus isnot required to maintain a fixed position during the scanning process.

After the patient has been placed into the scanner at step 504, theprocess 500 of FIG. 5 can move to step 506, wherein the patient isinstructed to begin continuously moving one or more portions of the bodythat are being scanned. Using the human head as an example, at step 506the patient can be instructed to move their head up and down and left toright in a continuous manner. In one or more examples, continuouslymoving the head can include tilting the head up and down and left toright in any direction that the patient desires at any moment of time,thus giving the patient flexibility to control their own movementwithout requiring any substantial coaching or instruction from the MRIoperator.

Once the patient begins the continuous motion at step 506, the process500 can move to step 508 wherein a determination is made as to whetherthe pulse sequence protocol has been completed thereby allowing forsufficient data (described in further detail below) to be acquired todetermine magnetic susceptibility. If at step 508, the pulse sequenceprotocol has been completed and sufficient data has been acquired thenthe process 500 can move to step 510 wherein the process is terminated.If however, at step 508 it is determined that the sufficient data hasnot been acquired, then the process 500 can wait at step 508 until it isdetermined that sufficient data has been acquired.

In comparing and contrasting the method of MRI phase data acquisitiondescribed in FIG. 2 with the example described in FIG. 5, it can bereadily apparent that the example described with respect to FIG. 5 canbe more efficient from a time perspective, and can be more comfortablefrom a patient's perspective. Rather than having to place a patient'sbody in specific orientations and hold the orientation for a longduration time, the process of FIG. 5 can allow the patient tocontinuously move the body part being imaged and for a shorter durationof time than the process described above with respect to FIG. 2.

In order to have a mathematically sufficient data set to calculatemagnetic susceptibility, the collection method described above withrespect to FIG. 5 can be modified to ensure that there is mathematicallysufficient data to solve the inverse problem of magnetic susceptibility.Since the method of FIG. 5 may not require the patient to move and holdtheir body in specific orientations, a modified technique to processingthe data can be employed to ensure that accurate magnetic susceptibilityimages are rendered even though the data at a particular orientation maybe sparse, i.e., the k-space data at the desired resolution have notbeen fully sampled/collected. Because the patient is moving continuouslyand in varied orientations, the acquired data can be categorizedspatially rather than temporally, and data that is spatially close toone another can be used to provide a mathematically sufficient data setso as to accurately calculate the magnetic susceptibility of tissuebeing analyzed.

FIG. 6 illustrates an exemplary method of processing the data collectedfrom an MRI system configured to acquire magnetic susceptibility oftissue within a patient according to examples of the disclosure. Theprocess 600 can begin at step 602 wherein the data is acquired from anMRI scanning procedure such as the one described above with respect toFIG. 5, using an MRI system such as the one described above with respectto FIG. 4. Once the data from the MRI scan has been acquired, theprocess 600 can move to step 604, wherein each data point acquired atstep 602 can be placed into one or more spatial bins.

In one or more examples, a spatial bin can refer to a two dimensional(or higher) range of motion that an acquired image datapoint can becategorized. The bin is determined based on the orientation of thepatient during data acquisition procedure described in FIG. 5. Asdescribed above with respect to FIG. 4, the MRI system motion caninclude a motion tracker 418 which can observe and record the exactposition of the patient at any given moment in time. For instance in oneexample, the motion tracker can include a video camera that can recordthe patient's movement and can determine the position of the patient'sbody in two or more dimensions (ie., X and Y for example). When a datapoint is acquired, the motion tracker 418 can provide the preciseorientation that the patient was in when that particular data wasacquired. In one or more examples, the data point can thus have positiondata associated with it in two or more dimensions. For instance, usingthe head as an example, at any moment in time during the acquisitionprocess, the orientation of the patient's head can be characterized intwo dimensions labeled as X and Y. X can represent the degree of tiltfrom shoulder to shoulder while Y can represent the degree of tilt fromlooking down to looking up. Thus at any given moment in time during theacquisition process, the motion tracker 418 can associate an orientationof the head (using the X,Y coordinates) to each and every data pointacquired at step 602.

In one or more examples, placing each data point into a bin can includecategorizing each data point into one or more bins with each bin definedby a specific range of X and Y values. FIG. 7 illustrates an exemplarygraphical representation of a binned data set according to examples ofthe disclosure. The example of FIG. 7 represents a visual representationof the bins described above for the purposes of explanation, however itshould be understood that the bins represent a way of organizing thedata acquired by the MRI scanner and thus the bins, in one or moreexamples, can be implemented in software rather than as a visualization.FIG. 7 illustrates a binning scheme 700, which has two dimensions 702and 704 labeled as X and Y in the figure respectively. Each dimension702 can be divided into one or more sections, thereby creating “bins”that represent a specific range of X values and Y values. For example,bin 708 can contain data acquired from a patient when the patient's headwas positioned in a certain range of X and Y values. While the datapoints (represented by a dot in the figure such as the one labeled 706)may have been acquired at different moments in time, they can be placedinto the same bin 708 since they were acquired at a moment when thepatient's head was in the specific range of X values and Y valuesassociated with bin 708.

In one or more examples, the size of the bins, as well as the range ofthe bins can be determined by the degree of movement exhibited by thepatient during the data acquisition process. In one or more examples,the process for establishing the bins described above can includedetermining the total range of motion exhibited by the patient duringthe data acquisition process, and then dividing the total range ofmotion in each dimensions into a pre-determined number of ranges,thereby forming bins.

Returning to the example of FIG. 6, once the data acquired at step 602has been placed into bins at step 604, the process 600 can move to step606 wherein the phase information for each data point can be calculatedusing compressive sensing reconstruction. At step 606, phase images canbe formed from the data at each bin, collected at step 602. However, asingle bin may not have an adequate amount of data to make the datasetmathematically sufficient to solve the inverse problem. Thus, in one ormore examples at step 606, in order to create a phase image at aparticular bin, the data that exists in the bins adjacent to the bin inwhich phase information is being reconstructed can be used to create themathematically sufficient dataset needed to create a phase image. Thistechnique can be referred to as “compressive sensing reconstruction.”

Referring back to the example of FIG. 7 to illustrate the concept of“compressive sensing reconstruction,” if a phase image were beingconstructed from the data at bin 708 only, the amount of data may not beenough to enable the inverse problem to be adequately solved. Thus, inone or more examples, in order to obtain an adequate number of datapoints while minimizing errors, the process at step 606 can use the dataat adjacent bins 710 a-h to create a mathematically adequate data set soas to create a high quality phase image. Because the data found in theadjacent bins 710 a-h were collected at positions that were spatiallyclose to the data collected in bin 708, the data can be used toreconstruct the phase image for bin 608 while minimizing error in theformation of the phase image. General mathematical framework is given inequations 12 & 13 below.

Once a phase image has been formed for each bin at step 606, the process600 can move to step 608, wherein the magnetic field for each bin can becalculated with respect to equation 2 described above.

Once the magnetic field is calculated at step 608, the process can moveto step 610 wherein the magnetic susceptibility of the tissue beingimaged can be calculated according to equations 4 and 5 described above.After calculating the magnetic susceptibility at step 610, the processcan move to step 612 where it is terminated.

The compressed sensing process described above with respect can provideone method of recovering susceptibility from phase data, but otherapproaches to recovering susceptibility can produce the same result,such as data-driven techniques and machine learning.

The object (i.e, the tissue to be sampled) can have a default alignmentto the magnetic field but can also be re-oriented with respect to themain magnetic field. For instance, let θ=[θ_(tip), θ_(tilt)] describethe variation in orientation from the aligned position. For example, ifthe imaging object is a patient's head, then the patient can tip itforward and back, or tilt it from shoulder to shoulder (and combinationsof those motions). In the continuous motion context, such motion isencouraged over the duration of the scan. Thus, the method can considera rotated version of the image of interest I_(c) (as obtained from coilc) from equation 1, namely, I_(c)(r, θ(t)) where the orientation θvaries over time t. It should be noted that the spatial coordinates rcan be fixed to the patient's frame of reference (as opposed to thescanner-frame of reference (as described above, which coincides with thepatient-frame of reference only when θ=[0,0]. In one or more examples,the spatial coordinates can also be fixed to the scanner's frame ofreference.

In one or more examples, and over the duration of the scan, data can beacquired by the MRI system over time. That data collection can bemodeled as discrete samples of the Fourier transform of the object,based on the solution to the Bloch equation with the scanner's gradientcoils used for spatial encoding. The collected data by each coil c canbe represented using equation 8 below:

d _(c)(k(t))=S(k(t))∘F(I_(c)(r, θ(t))).   (equation 8)

In equation 8 above, F can represent the Fourier transform, mappingI_(c)(r, θ(t)) to its spectrum over spatial frequencies k=[k_(x), k_(y),k_(z)], and S(k(t)) can represent a binary mask with 1 in the specificcoordinates discrete spatial frequencies that were measured by the MRIsystem protocol (and 0 elsewhere). d_(d c) can be computed frompointwise multiplication (i.e. Hadamard product) of the mask with thespectrum. In conventional MRI, S(k(t)) is a uniform grid and theequation reduces to the discrete Fourier transform. Furthermore, thetransform F can be modulated by the MRI receiver-coil sensitivities.

In the examples described above, S(k(t)) may be uniform or non-uniformsamples, and furthermore they represent the scanner-frame of reference.The examples described above can employ a motion registration method(described above) to measure the orientation of the object over time,i.e. θ(t). From the motion registration, the data can be correspondinglyrotated (via rotation matrix multiplication) to the patient-frame ofreference. Equation 9 below, represent equation 8 above in terms of thepatient's frame of reference.

{tilde over (d)} _(c)({tilde over (k)}, θ(t))=R _(θ(t)) ·d _(c)(k(t))  (equation 9)

For conciseness of notation, combine these linear operations (Fouriertransform, sampling, and registration) such that

{tilde over (d)} _(c)({tilde over (k)}, θ(t))=F _(R,S)(I _(c)(r, θ(t)))  (equation 10)

Finally, the orientation-registered Fourier measurements {tilde over(d)} _(c) are input into the reconstruction pipeline. The phase ofI_(c)(r, θ(t)), ϕ(r, θ(t)), is recovered from {tilde over (d)}_(c)({tilde over (k)}, θ(t)) according to the compressed sensingframework described above (also see equation 12 & 13).

Once the phase image is reconstructed, the resulting phase image can beused to provide a mapping of the magnetic susceptibility of the scannedobject. The phase ϕ(r, θ(t)) matters in so much as it informs themeasurement of magnetic susceptibility X(r). X does not depend on theorientation θ. θ is varied intentionally so that incomplete measurementsare overcome as described above.

The inverse problem of determining X from ϕ(r, θ(t)) can be layered atopthe inverse problem of recovering ϕ from {tilde over (d)} _(c). Theultimate objective can be to recover an accurate susceptibility map fromthe measured data. Equation 11 below provides an exemplary relationshipbetween the ϕ and {tilde over (d)}

$\begin{matrix}{{\overset{\hat{}}{\chi}(r)} = {\underset{\chi}{\arg\min}{{{{\overset{˜}{d}}_{c}\left( {k,{\theta(t)}} \right)} - {F_{R,S}\left( {{T_{c}(r)}{{m(r)} \cdot e^{{- 2}\;\pi\;{i \cdot {({{h{({r,{\theta{(t)}}})}}*{\chi{(r)}}})}}\gamma\;{TE}}}} \right)}}}_{c}}} & \left( {{equation}\mspace{20mu} 11} \right)\end{matrix}$

A reasonable approach to solving such a layered problem is to break itdown into components. For example, first recovering the phase and thenperforming deconvolution. One approach for recovering phase is to usecompressed sensing. In this approach, the continuous object orientationsθ(t) must first be discretized into bins (θ_(n)) as described above withrespect to FIGS. 5-7. Compressed sensing can be useful to generate aphase image from continuous motion data because the acquired data {tildeover (d)} _(c) associated with each θ bin may be undersampled. That is,{tilde over (d)} _(c)(k, θ_(n)) does not include sufficient data on itsown to solve the linear inverse problem for ϕ(r, θ_(n)). Compressedsensing is a nonlinear reconstruction approach in which the datafidelity is weighed against prior assumptions (involving sparsity) toprovide an optimal solution to the inverse problem. Equation 12 showsone example compressed sensing framework which can yield the desiredphase (or field data)

                                     (equation  12)${\overset{\hat{}}{m}(r)},{{\delta{B_{\chi}\left( {r,\theta_{n}} \right)}} = {{\underset{m,{\delta\; B_{\chi}}}{\arg\min}{{{{\overset{\sim}{d}}_{c}\left( {k,\theta_{n}} \right)} - {F_{R,S}\left( {{T_{c}(r)}{{m(r)} \cdot e^{{- 2}\;\pi\; i\;{\gamma{({{\delta\;{B_{\chi}{({r,\theta_{n}})}}} + {\delta\;{B_{0}{({r,\theta_{n}})}}}})}}{TE}}}} \right)}}}_{c}^{2}} + {\quad{{R\left( {m,\phi} \right)}}^{1}}}}$

In equation 12 above, we replaced ϕ(r, θ_(n)) with its dependence onδB_(X)(r, θ_(n)) from Equations 2 & 3. As discussed above, thebackground field δB₀(r, θ_(n)) can either be estimated using existingBackground Field Removal algorithms, or, alternatively, it can bejointly estimated with Equation 12 as well. The

₂ term (superscript 2 term) can ensure data fidelity, and the

₁term (denoted by superscript 1) can incorporate sparse regularization(i.e., using data from adjacent bins to reconstruct the phase image at aparticular location). In one example, ∥R (m, ϕ)∥¹ measures the totalvariation of the object across θ_(n) as denoted by equation 13 below.

$\begin{matrix}{{R\left( {m,\phi} \right)} = {\sum\limits_{\theta_{n}}{{{{m(r)} \cdot e^{{- 2}\pi\;{i \cdot {\phi{({r,\theta_{n}})}}}}} - {{m(r)} \cdot e^{{- 2}\pi\;{i \cdot {\phi{({r,\theta_{n + 1}})}}}}}}}}} & \left( {{equation}\mspace{20mu} 13} \right)\end{matrix}$

In the example of equation 13, the sparse regularization can leveragethat the absolute differences between adjacent θ_(n) bins are sparse.The mathematical assumption is that the terms of the difference aresimilar because m(r) does not vary with rotation, and θ_(n) is spatiallynearby θ_(n+1). Thus the examples above exploit this similarity, andregularize the problem so that the resulting total variation is sparse(and relaxed from

₀ to

₁).

Once the reconstructed phase image is generated using the methodsdescribed above the dipole inversion step can be computed linearly—froman overdetermined system of equations, see equation 6. The dipoleinversion can furthermore take into consideration the distribution ofthe patient motion θ(t) within each bin θ_(n) as described above.

The system described in FIG. 4, the method of data collection describedin FIG. 5, and the method of processing the data described in FIG. 6 canoverall lead to a process for generating magnetic susceptibilitymeasurements that can significantly reduce imaging time, and improve theaccuracy in magnetic susceptibility measurements.

FIG. 8 illustrates an example of a computing device in accordance withone embodiment. Device 800 can be a host computer connected to anetwork. Device 800 can be a client computer or a server. As shown inFIG. 8, device 800 can be any suitable type of microprocessor-baseddevice, such as a personal computer, workstation, server, or handheldcomputing device (portable electronic device), such as a phone ortablet. The device can include, for example, one or more of processors802, input device 806, output device 808, storage 810, and communicationdevice 804. Input device 806 and output device 808 can generallycorrespond to those described above and can either be connectable orintegrated with the computer.

Input device 806 can be any suitable device that provides input, such asa touch screen, keyboard or keypad, mouse, or voice-recognition device.Output device 808 can be any suitable device that provides output, suchas a touch screen, haptics device, or speaker.

Storage 810 can be any suitable device that provides storage, such as anelectrical, magnetic, or optical memory, including a RAM, cache, harddrive, or removable storage disk. Communication device 804 can includeany suitable device capable of transmitting and receiving signals over anetwork, such as a network interface chip or device. The components ofthe computer can be connected in any suitable manner, such as via aphysical bus or wirelessly.

Software 812, which can be stored in storage 810 and executed byprocessor 802, can include, for example, the programming that embodiesthe functionality of the present disclosure (e.g., as embodied in thedevices as described above).

Software 812 can also be stored and/or transported within anynon-transitory computer-readable storage medium for use by or inconnection with an instruction execution system, apparatus, or device,such as those described above, that can fetch instructions associatedwith the software from the instruction execution system, apparatus, ordevice and execute the instructions. In the context of this disclosure,a computer-readable storage medium can be any medium, such as storage810, that can contain or store programming for use by or in connectionwith an instruction execution system, apparatus, or device.

Software 812 can also be propagated within any transport medium for useby or in connection with an instruction execution system, apparatus, ordevice, such as those described above, that can fetch instructionsassociated with the software from the instruction execution system,apparatus, or device and execute the instructions. In the context ofthis disclosure, a transport medium can be any medium that cancommunicate, propagate, or transport programming for use by or inconnection with an instruction execution system, apparatus, or device.The transport readable medium can include, but is not limited to, anelectronic, magnetic, optical, electromagnetic, or infrared wired orwireless propagation medium.

Device 800 may be connected to a network, which can be any suitable typeof interconnected communication system. The network can implement anysuitable communications protocol and can be secured by any suitablesecurity protocol. The network can comprise network links of anysuitable arrangement that can implement the transmission and receptionof network signals, such as wireless network connections, T1 or T3lines, cable networks, DSL, or telephone lines.

Device 800 can implement any operating system suitable for operating onthe network. Software 812 can be written in any suitable programminglanguage, such as C, C++, Java, or Python. In various embodiments,application software embodying the functionality of the presentdisclosure can be deployed in different configurations, such as in aclient/server arrangement or through a Web browser as a Web-basedapplication or Web service, for example.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the disclosure to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the techniques and their practical applications. Othersskilled in the art are thereby enabled to best utilize the techniquesand various embodiments with various modifications as are suited to theparticular use contemplated.

Although the disclosure and examples have been fully described withreference to the accompanying figures, it is to be noted that variouschanges and modifications will become apparent to those skilled in theart. Such changes and modifications are to be understood as beingincluded within the scope of the disclosure and examples as defined bythe claims.

This application discloses several numerical ranges in the text andfigures. The numerical ranges disclosed inherently support any range orvalue within the disclosed numerical ranges, including the endpoints,even though a precise range limitation is not stated verbatim in thespecification because this disclosure can be practiced throughout thedisclosed numerical ranges.

The above description is presented to enable a person skilled in the artto make and use the disclosure and is provided in the context of aparticular application and its requirements. Various modifications tothe preferred embodiments will be readily apparent to those skilled inthe art, and the generic principles defined herein may be applied toother embodiments and applications without departing from the spirit andscope of the disclosure. Thus, this disclosure is not intended to belimited to the embodiments shown but is to be accorded the widest scopeconsistent with the principles and features disclosed herein. Finally,the entire disclosure of the patents and publications referred in thisapplication are hereby incorporated herein by reference.

1. A system for scanning for and obtaining magnetic resonance imaging (MRI) phase data from a patient undergoing an MRI scan, the system comprising: one or more magnets; one or more radio frequency (RF) coils, wherein the one or more RF coils are configured to transmit and receive RF signals; a memory; one or more processors; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs when executed by the one or more processors cause the processor to: generate one or more magnetic fields using one or more magnets; transmit one or more RF signals to the patient, wherein the one or more RF signals are generated and transmitted by one or more RF coils configured to generate and transmit RF signals; receive a plurality RF signals from the one or more RF coils transmitted from the patient in response to the generated one or more magnetic fields and the one or more generated RF signals, wherein the received plurality of RF signals are transmitted from the patient while the patient is continuously moving during the MRI scan; receive data corresponding to a plurality of physical orientations of the patient and the time at which the patient was determined to be in each physical orientation of the plurality of physical orientations while continuously moving during the MRI scan; associate each RF signal of the received plurality RF signals with a physical orientation of the patient based on the received data; reconstruct a phase image associated with each of the received one or more RF signals from the patient based on the received one or more RF signals and the received data corresponding to the plurality of physical orientations of the patient and the time at which the patient was determined to be in each physical orientation of the plurality of physical orientations while continuously moving during the MRI scan, wherein reconstructing a phase image associated with the received one or more RF signals from the patient comprises: ; and categorizing each received RF signal of the plurality RF signals into a bin of a plurality of bins based on the received data associated with the RF signal , wherein each bin of the plurality of bins corresponds to a range of values associated with the physical orientation of patient and determining a phase information of a first RF signal categorized into a first bin of the plurality of bins based on the received RF signals categorized into the first bin and the received RF signals categorized into one or more bins adjacent to the bin, wherein the range of values associated with each adjacent bin is proximal to the range of values associated with the first bin; and generate a magnetic susceptibility image of the patient based on the reconstructed phase image.
 2. The system of claim 1, wherein the received data corresponding to a plurality of physical orientations of the patient and the time at which the patient was determined to be in each physical orientation of the plurality of physical orientations while continuously moving during the MRI scan is received from a motion tracker.
 3. (canceled)
 4. The system of claim 1, wherein the one or more processors are caused to determine a magnetic susceptibility of a portion of the patient's body based on the determined phase information of the first RF signal.
 5. The system of claim 4, wherein determining the magnetic susceptibility of the portion of the patient's body comprises: calculating a magnetic field of the portion of the patient's body based on the determined phase information of the first RF signal; and calculating the magnetic susceptibility of the portion of the patient's body based on the determined phase information of the first RF signal.
 6. The system of claim 1, wherein the one or more processors are caused to generate an image of the portion of the patient's body based on the determined magnetic susceptibility of the portion of the patient's body.
 7. The system of claim 2, wherein the motion tracker includes a camera configured to capture the orientation of a portion of the patient's body.
 8. The system of claim 1, wherein a pulse sequencer is used to cause the one or more magnets to generate one or more magnetic fields, and wherein the pulse sequencer is used to cause to one or more RF coils to transmit the one or more RF signals to the patient.
 9. The system of claim 8, wherein the pulse sequencer is configured to generate the one or more magnetic fields and the one or more RF signals so as to calculate the magnetic susceptibility of a portion of the patient's body.
 10. The system of claim 9, wherein the pulse sequencer is configured to generate the one or more magnetic fields and the one or more RF signals based on a portion of the patient's body being scanned.
 11. The system of claim 2, wherein the motion tracker collects data corresponding to the plurality of physical orientations of the patient in a first spatial dimension and a second spatial dimension.
 12. The system of claim 11, wherein each bin of the plurality of bins corresponds to a range of values in the first dimension and range of values in the second dimension.
 13. The system of claim 1, wherein reconstructing the phase image includes converting the received RF signals to phase information.
 14. A method for scanning for and obtaining magnetic resonance imaging (MRI) phase data from a patient undergoing an MRI scan, the method comprising: generating one or more magnetic fields using one or more magnets; transmitting one or more RF signals to a patient, wherein the one or more RF signals are generated and transmitted by one or more RF coils configured to generate and transmit RF signals; receiving a plurality RF signals from the one or more RF coils transmitted from the patient in response to the generated one or more magnetic fields and the one or more generated RF signals, wherein the received plurality of RF signals are transmitted from the patient while the patient is continuously moving during the MRI scan; receiving data corresponding to a plurality of physical orientations of the patient and the time at which the patient was determined to be in each physical orientation of the plurality of physical orientations while continuously moving during the MRI scan; associating each RF signal of the received plurality RF signals with a physical orientation of the patient based on the received data; reconstructing a phase image associated with each of the received one or more RF signals from the patient based on the received one or more RF signals and the received data corresponding to the plurality of physical orientations of the patient and the time at which the patient was determined to be in each physical orientation of the plurality of physical orientations while continuously moving during the MRI scan, wherein reconstructing a phase image associated with the received one or more RF signals from the patient comprises: categorizing each received RF signal of the plurality RF signals into a bin of a plurality of bins based on the received data associated with the RF signal , wherein each bin of the plurality of bins corresponds to a range of values associated with the physical orientation of patient and determining a phase information of a first RF signal categorized into a first bin of the plurality of bins based on the received RF signals categorized into the first bin and the received RF signals categorized into one or more bins adjacent to the bin, wherein the range of values associated with each adjacent bin is proximal to the range of values associated with the first bin; and generating a magnetic susceptibility image of the patient based on the reconstructed phase image.
 15. The method of claim 14, wherein the received data corresponding to a plurality of physical orientations of the patient and the time at which the patient was determined to be in each physical orientation of the plurality of physical orientations while continuously moving during the MRI scan is received from a motion tracker.
 16. (canceled)
 17. The method of claim 14, wherein the one or more processors are caused to determine a magnetic susceptibility of a portion of the patient's body based on the determined phase information of the first RF signal.
 18. The method of claim 17, wherein determining the magnetic susceptibility of the portion of the patient's body comprises: calculating a magnetic field of the portion of the patient's body based on the determined phase information of the first RF signal; and calculating the magnetic susceptibility of the portion of the patient's body based on the determined phase information of the first RF signal.
 19. The method of claim 14, wherein the method comprises generating an image of the portion of the patient's body based on the determined magnetic susceptibility of the portion of the patient's body.
 20. The method of claim 15, wherein the motion tracker includes a camera configured to capture the orientation of a portion of the patient's body.
 21. The method of claim 14, wherein a pulse sequencer is used to cause the one or more magnets to generate one or more magnetic fields, and wherein the pulse sequencer is used to cause to one or more RF coils to transmit the one or more RF signals to the patient.
 22. The method of claim 21, wherein the pulse sequencer is configured to generate the one or more magnetic fields and the one or more RF signals so as to calculate the magnetic susceptibility of a portion of the patient's body.
 23. The method of claim 22, wherein the pulse sequencer is configured to generate the one or more magnetic fields and the one or more RF signals based on a portion of the patient's body being scanned.
 24. The method of claim 15, wherein the motion tracker collects data corresponding to the plurality of physical orientations of the patient in a first spatial dimension and a second spatial dimension.
 25. The method of claim 24, wherein each bin of the plurality of bins corresponds to a range of values in the first dimension and range of values in the second dimension.
 26. The method of claim 14, wherein reconstructing the phase image includes converting the received RF signals to phase information.
 27. A non-transitory computer-readable storage medium comprising one or more programs for scanning for and obtaining magnetic resonance imaging (MRI) phase data from a patient undergoing an MRI scan, wherein the one or more programs, when executed by one or more processors, cause the one or more processors to: generate one or more magnetic fields using one or more magnets; transmit one or more RF signals to a patient, wherein the one or more RF signals are generated and transmitted by one or more RF coils configured to generate and transmit RF signals; receive a plurality RF signals from the one or more RF coils transmitted from the patient in response to the generated one or more magnetic fields and the one or more generated RF signals, wherein the received plurality of RF signals are transmitted from the patient while the patient is continuously moving during the MRI scan; receive data corresponding to a plurality of physical orientations of the patient and the time at which the patient was determined to be in each physical orientation of the plurality of physical orientations while continuously moving during the MRI scan; associate each RF signal of the received plurality RF signals with a physical orientation of the patient based on the received data; reconstruct a phase image associated with each of the received one or more RF signals from the patient based on the received one or more RF signals and the received data corresponding to the plurality of physical orientations of the patient and the time at which the patient was determined to be in each physical orientation of the plurality of physical orientations while continuously moving during the MRI scan, wherein reconstructing a phase image associated with the received one or more RF signals from the patient comprises: categorizing each received RF signal of the plurality RF signals into a bin of a plurality of bins based on the received data associated with the RF signal , wherein each bin of the plurality of bins corresponds to a range of values associated with the physical orientation of patient; and determining a phase information of a first RF signal categorized into a first bin of the plurality of bins based on the received RF signals categorized into the first bin and the received RF signals categorized into one or more bins adjacent to the bin, wherein the range of values associated with each adjacent bin is proximal to the range of values associated with the first bin; and generate a magnetic susceptibility image of the patient based on the reconstructed phase image.
 28. The non-transitory computer-readable storage medium of claim 27, wherein the received data corresponding to a plurality of physical orientations of the patient and the time at which the patient was determined to be in each physical orientation of the plurality of physical orientations while continuously moving during the MRI scan is received from a motion tracker.
 29. (canceled)
 30. The non-transitory computer-readable storage medium of claim 27, wherein the one or more processors are caused to determine a magnetic susceptibility of a portion of the patient's body based on the determined phase information of the first RF signal.
 31. The non-transitory computer-readable storage medium of claim 30, wherein determining the magnetic susceptibility of the portion of the patient's body comprises: calculating a magnetic field of the portion of the patient's body based on the determined phase information of the first RF signal; and calculating the magnetic susceptibility of the portion of the patient's body based on the determined phase information of the first RF signal.
 32. The non-transitory computer-readable storage medium of claim 27, wherein the method comprises generating an image of the portion of the patient's body based on the determined magnetic susceptibility of the portion of the patient's body.
 33. The non-transitory computer-readable storage medium of claim 28, wherein the motion tracker includes a camera configured to capture the orientation of a portion of the patient's body.
 34. The non-transitory computer-readable storage medium of claim 27, wherein a pulse sequencer is used to cause the one or more magnets to generate one or more magnetic fields, and wherein the pulse sequencer is used to cause to one or more RF coils to transmit the one or more RF signals to the patient.
 35. The non-transitory computer-readable storage medium of claim 34, wherein the pulse sequencer is configured to generate the one or more magnetic fields and the one or more RF signals so as to calculate the magnetic susceptibility of a portion of the patient's body.
 36. The non-transitory computer-readable storage medium of claim 35, wherein the pulse sequencer is configured to generate the one or more magnetic fields and the one or more RF signals based on a portion of the patient's body being scanned.
 37. The non-transitory computer-readable storage medium of claim 28, wherein the motion tracker collects data corresponding to the plurality of physical orientations of the patient in a first spatial dimension and a second spatial dimension.
 38. The non-transitory computer-readable storage medium of claim 37, wherein each bin of the plurality of bins corresponds to a range of values in the first dimension and range of values in the second dimension.
 39. The method of claim 27, wherein reconstructing the phase image includes converting the received RF signals to phase information 